We are witnessing an unprecedented growth in the amount of data that is being collected and made available for data mining. While the availability of large-scale datasets presents exciting opportunities for advancing sciences, healthcare, understanding of human behavior etc., mining the data set for useful information becomes a computationally challenging task. We are in an era where the volume of data is growing faster than the rate at which available computing power is growing, thereby creating a dire need for computationally efficient algorithms for data mining.
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Abstract: In this talk, I will present a novel blind image quality assessment (BIQA) algorithm inspired by the sparse representation of natural images in the human visual system (HVS). The hypothesis behind the proposed method is that the properties of natural images that afford their sparse representation are altered in the presence of distortion. The change in sparsity is quantified to show that it is indeed a measure of the unnaturalness or distortion in an image.
Abstract: The role of image quality assessment in tasks such as (i) the fusion of long wave infrared (LWIR) and visible images and (ii) face recognition in LWIR images has not been researched extensively from the natural scene statistics (NSS) perspective. For instance, even though there are several well-known measures that quantify the quality of fused images, there has been little work done on analyzing the statistics of fused LWIR and visible images and associated distortions.